A new approach to inspect the internal pressure of vacuum switchgear

A new approach is developed to inspect the internal pressure within the vacuum switchgear by no means of magnet-exciting coil, micro-discharge threshold voltage Ud and field emission threshold voltage Ue, all of which are available in laboratory. Experimental results show that internal pressure is a function of the ratio of the micro-discharge threshold voltage to the field emission threshold voltage i.e., Ud/Ue. By this method, the internal pressure in the range of 100-10-4 Pa within vacuum chambers can be inspected without magnet-exciting coil which is required in conventional magnetron discharge method.     

Simple Approach on Intermixing Solar and Wind Energy and Minimizing Their Intermittent Effect

Renewable energy resources especially wind and solar energy are emerging as the modern power sources to electrify remote areas. The main reason behind their emergence is due to their environment-friendly behavior,unlimited availability and short period for replenishment over nonrenewable energy resources. Renewable energy resources are much better than nonrenewable energy resources, but the intermittency in renewable energy resources degrades the system performance. In order to overcome the intermittency, multiple hybrid system techniques were proposed in literature that can achieve suitable results but have disadvantages of complicated control structures and high implementation cost. Considering aforementioned shortcomings, a simple balancing approach is proposed to intermix solar and wind energy together so as to utilize the available energy from both sources at a given time.It is very common that solar farms are the dominating source of energy in daytime and summer, while wind farms are the dominating source of energy at night and in winter. The proposed approach delivers maximum possible power to the load by combining dominating and non-dominating resources all the time, hence mitigating the intermittency of individual resources. Compared with other approaches, the proposed approach offers key benefits with redundancy, simple design and low cost, which can be analyzed from simulation results.     

Logistic Regression Based Arc Fault Detection in Photovoltaic Systems Under Different Conditions

This paper investigates direct current(DC) arc fault detection in photovoltaic system. In order to avoid the risk of fire ignition caused by the arc fault in the photovoltaic power supply, it is urgent to detect the DC arc fault in the photovoltaic system. Once an arc fault is detected, the power supply should be cut off immediately. A lot of field experiments are carried out to obtain the data of arc fault current of the photovoltaic system under different current conditions. Cable length, arc gap, and the effects of different sensors are tested.These three conditions are the most significant features of this paper. Four characteristic variables from both the time domain and the frequency domain are extracted to identify the arc fault. Then the logistic regression method in the field of artificial intelligence and machine learning is originally used to analyze the experimental results of arc fault in the photovoltaic system. The function between the probability of the arc fault and the change of the characteristic variables is obtained. After validating 80 groups of experimental data under different conditions,the accuracy rate of the arc fault detection by this algorithm is proved to reach 100%.